Few-Shot Learning for Rooftop Detection in Satellite Imagery

Deep Learning Tutorial

Giorgio Coppala, Nadine Daum, Elena Dreyer, Nico Reichardt

Policy Relevance

  • Many public auhorities face the problem of limited labeled data (annotation is expensive, slow, or requires domain expertise)

  • Applications:

    • medical sector: rare disease detection
    • emergency management: flood extent mapping
    • climate & energy: solar PV rooftop assessment
    • urban planning: building footprints & infrastructure mapping

  • Few-shot learning (FSL) can help:

    • Learns to generalize from 1–5 labeled support examples per class
    • (in our case) learns a feature embedding and constructs class prototypes
    • Enables segmentation in a new city with minimal additional annotation

Problem Setting

  • Goal of the tutorial: apply Prototypical Networks to rooftop segmentation using only a few labeled tiles

  • Few-shot segmentation allows the model to learn characteristic rooftop shapes and textures from a small Geneva subset

  • Demonstrates how rooftop maps can be produced for solar potential estimation in a new geographic setting with limited labels

Demonstration use case (self-made visualization)

Dataset: Roofs of Geneva

  • Size: 1,050 labeled image-mask pairs

  • Task: Binary segmentation masks (rooftop vs background)

  • Geographic splits: 3 grids/ neighborhoods (North, Center, South)

  • Image size: 250x250 pixels

  • Categories: Industrial, Residential

Inside the dataset

Geneva Animation: raw image → overlay rooftop → binary mask

Discussion

Room for improvement:

  • Fine-tune / tweak model parameters
    • Add regularization
    • Increase number of epochs
  • Implement rough approximation of solar potential
    • e.g. based on IoU over roof area

Open for discussion:

  • Try a different encoder ?
    • e.g. ResNet-50
  • Change train / test split strategy ?
    • e.g. random shuffle regardless of geographic regions

GitHub Repo

References

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